Is the Group Structure Important in Grouped Functional Time Series?

Yang, Yang; Shang, Han Lin

doi:10.6339/21-JDS1031

Journal of Data Science

Is the Group Structure Important in Grouped Functional Time Series?

Volume 20, Issue 3 (2022): Special Issue: Data Science Meets Social Sciences, pp. 303–324

Yang Yang

Han Lin Shang

https://doi.org/10.6339/21-JDS1031

Pub. online: 4 January 2022 Type: Statistical Data Science

Open Access

Received
30 August 2021

Accepted
8 November 2021

Published
4 January 2022

Abstract

We study the importance of group structure in grouped functional time series. Due to the non-uniqueness of group structure, we investigate different disaggregation structures in grouped functional time series. We address a practical question on whether or not the group structure can affect forecast accuracy. Using a dynamic multivariate functional time series method, we consider joint modeling and forecasting multiple series. Illustrated by Japanese sub-national age-specific mortality rates from 1975 to 2016, we investigate one- to 15-step-ahead point and interval forecast accuracies for the two group structures.

Supplementary material

Supplementary Material

For reproducibility, all R codes are collated in a Github repository at https://github.com/hanshang/GMFTS. This repository contains the following files: • README: Describe the functionality of each R file. • R files with indices 1 to 29: Functions used to compute numerical results presented in the paper. • Example: Contains a quick example of forecasting prefecture-level age-specific mortality rates using the DMFTS method.

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2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

dynamic principal component analysis forecast reconciliation Japanese sub-national age-specific mortality rates long-run covariance function multivariate functional principal component analysis

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